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Nonconvexities in Stochastic Control Models

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  • Amman, Hans M
  • Kendrick, David A

Abstract

Nonconvexities in the criterion function of adaptive control problems were first found about ten years ago with numerical methods. Recently they have been confirmed by B. Mizrach (1991) with analytical methods. He found that a source of the nonconvexity was the probing component of the cost-to-go. Mizrach's results have been extended in this paper. First, the probing function has been characterized and found to support the use of algorithms that exploit this character to find the global optimum. Secondly, a new source of nonconvexities has been found in the cautionary component of the cost-to-go. Copyright 1995 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Suggested Citation

  • Amman, Hans M & Kendrick, David A, 1995. "Nonconvexities in Stochastic Control Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(2), pages 455-475, May.
    • Handle: RePEc:ier:iecrev:v:36:y:1995:i:2:p:455-75
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    Cited by:

    1. Peter John Robinson & W.J.W. Botzen & F. Zhou, 2019. "An experimental study of charity hazard: The effect of risky and ambiguous government compensation on flood insurance demand," Working Papers 19-19, Utrecht School of Economics.
    2. Amman, Hans M & Kendrick, David A, 1999. "Should Macroeconomic Policy Makers Consider Parameter Covariances?," Computational Economics, Springer;Society for Computational Economics, vol. 14(3), pages 263-267, December.
    3. repec:use:tkiwps:2020 is not listed on IDEAS
    4. Tucci, Marco P. & Kendrick, David A. & Amman, Hans M., 2010. "The parameter set in an adaptive control Monte Carlo experiment: Some considerations," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1531-1549, September.
    5. Wieland, Volker, 2000. "Monetary policy, parameter uncertainty and optimal learning," Journal of Monetary Economics, Elsevier, vol. 46(1), pages 199-228, August.
    6. Volker Wieland, "undated". "Monetary Policy and Uncertainty about the Natural Unemployment Rate," Computing in Economics and Finance 1997 11, Society for Computational Economics.
    7. D.A. Kendrick & H.M. Amman & M.P. Tucci, 2008. "Learning About Learning in Dynamic Economic Models," Working Papers 08-20, Utrecht School of Economics.
    8. Hans Amman & David Kendrick, 2014. "Comparison of policy functions from the optimal learning and adaptive control frameworks," Computational Management Science, Springer, vol. 11(3), pages 221-235, July.
    9. Amman, Hans M. & Kendrick, David A. & Tucci, Marco P., 2020. "Approximating The Value Function For Optimal Experimentation," Macroeconomic Dynamics, Cambridge University Press, vol. 24(5), pages 1073-1086, July.
    10. Kendrick, David A., 2005. "Stochastic control for economic models: past, present and the paths ahead," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 3-30, January.
    11. H.M. Amman & D.A. Kendrick, 2012. "Conjectures on the policy function in the presence of optimal experimentation," Working Papers 12-09, Utrecht School of Economics.
    12. Zeyi Fu & Hongli Niu & Weiqing Wang, 2023. "Market Efficiency and Cross-Correlations of Chinese New Energy Market with Other Assets: Evidence from Multifractality Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 1287-1311, October.
    13. Wieland, Volker, 2000. "Learning by doing and the value of optimal experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 24(4), pages 501-534, April.
    14. Amman, Hans M. & Neudecker, Heinz, 1997. "Numerical solutions of the algebraic matrix Riccati equation," Journal of Economic Dynamics and Control, Elsevier, vol. 21(2-3), pages 363-369.
    15. Amman, Hans M. & Kendrick, David A., 1997. "Active learning: A correction," Journal of Economic Dynamics and Control, Elsevier, vol. 21(10), pages 1613-1614, August.
    16. Tim Willems, 2017. "Actively Learning by Pricing: A Model of an Experimenting Seller," Economic Journal, Royal Economic Society, vol. 127(604), pages 2216-2239, September.
    17. V. Blueschke-Nikolaeva & D. Blueschke & R. Neck, 2020. "OPTCON3: An Active Learning Control Algorithm for Nonlinear Quadratic Stochastic Problems," Computational Economics, Springer;Society for Computational Economics, vol. 56(1), pages 145-162, June.
    18. Beck, Gunter W. & Wieland, Volker, 2002. "Learning and control in a changing economic environment," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1359-1377, August.
    19. Tucci, Marco P., 2002. "A note on global optimization in adaptive control, econometrics and macroeconomics," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1739-1764, August.
    20. Hans M. Amman & Marco P. Tucci, 2017. "The DUAL Approach in an Infinite Horizon Model," Department of Economics University of Siena 766, Department of Economics, University of Siena.
    21. Marco Tucci, 2006. "Understanding the Difference Between Robust Control and Optimal Control in a Linear Discrete-Time System with Time-Varying Parameters," Computational Economics, Springer;Society for Computational Economics, vol. 27(4), pages 533-558, June.
    22. D. Blueschke & V. Blueschke-Nikolaeva & R. Neck, 2013. "Stochastic Control of Linear and Nonlinear Econometric Models: Some Computational Aspects," Computational Economics, Springer;Society for Computational Economics, vol. 42(1), pages 107-118, June.
    23. Bond, Craig A., 2008. "On the Potential Use of Adaptive Control Methods for Improving Adaptive Natural Resource Management," Working Papers 108721, Colorado State University, Department of Agricultural and Resource Economics.

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